# Deep Neural Maps

arXiv: https://arxiv.org/abs/1810.07291 · HTML: https://mlsanity.com/paper/1810.07291/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2018.

## Built on by (most cited first, 2 of 2)

- [SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series](https://mlsanity.com/paper/2205.15875/) (2022)
- [Cortex Inspired Learning to Recover Damaged Signal Modality with ReD-SOM Model](https://mlsanity.com/paper/2307.15095/) (2023)

## Abstract (arXiv)

We introduce a new unsupervised representation learning and visualization using deep convolutional networks and self organizing maps called Deep Neural Maps (DNM). DNM jointly learns an embedding of the input data and a mapping from the embedding space to a two-dimensional lattice. We compare visualizations of DNM with those of t-SNE and LLE on the MNIST and COIL-20 data sets. Our experiments show that the DNM can learn efficient representations of the input data, which reflects characteristics of each class. This is shown via back-projecting the neurons of the map on the data space.